Affiliations:
  1. Arizona State University School of Life Sciences
  2. Max Planck Institute for Evolutionary Anthropology
  3. University of California Santa Barbara

*Corresponding author: KB McCune ()

Cite as: Berens JM, Logan CJ, Folsom M, Sevchik A, Bergeron L, McCune KB. Submitted to PCI Ecology Nov 2020. Validating morphological condition indices and their relationship with reproductive success in great-tailed grackles.

This preregistration has been pre-study peer reviewed and received an In Principle Acceptance by:

Marcos Mendez (2019 In Principle Acceptance) Are condition indices positively related to each other and to fitness?: a test with grackles. Peer Community in Ecology, 100035. 10.24072/pci.ecology.100035

  • Reviewers: Javier Seoane and Isabel López-Rull

ABSTRACT

Morphological variation among individuals has the potential to influence multiple life history characteristics such as dispersal, migration, reproductive success, and survival (Wilder et al. 2016). Individuals that are in better “condition” can disperse or migrate further or more successfully, have greater reproductive success, and survive longer (Heidinger et al. 2010; Liao et al. 2011; Wilder et al. 2016), particularly in years where environmental conditions are harsh (Milenkaya et al. 2015). Body condition is defined in various ways, but is most often measured using an individual’s energetic or immune state (Milenkaya et al. 2015). These traits are difficult to measure directly, therefore a variety of morphological proxies to quantify condition are used instead, including fat score (Kaiser 1993), weight, ratio of weight to tarsus length (Labocha et al. 2014), a scaled mass index (Peig and Green 2009), as well as hematological indices for immune system function (Fleskes et al. 2017; Kraft et al. 2019). However, there is mixed support regarding whether these condition indices relate to life history characteristics (Labocha et al. 2014; Wilder et al. 2016), and whether the relationship is linear (McNamara et al. 2005; Milenkaya et al. 2015). Additionally, although some investigations use multiple morphological proxies for condition (e.g. Warnock and Bishop 1998), rarely have there been direct comparisons among proxies to validate that they measure the same trait. In this investigation, we define condition as an energetic state and we attempt to measure it by comparing two indices (fat score and the scaled mass index) to validate whether they measure the same trait and whether they correlate with measures of reproductive success in our study system, the great-tailed grackle (Quiscalus mexicanus). We found that the morphological proxies did not correlate with each other, indicating that they do not measure the same trait. Further, neither proxy correlated with reproductive success in males, measured as whether a male held a territory containing nests or not. We found that females with a high scaled mass index had a significantly lower probability that their nest would survive on any given day. However, there was no relationship between female fat score and nest survival. These results indicate that measures of condition should be validated before relying on their use as a condition proxy in grackles and birds in general. Future research should further investigate our unexpected result that higher scaled mass index correlated with lower nest survival to better understand the importance of energetic condition for reproductive success - a necessary component for selection to act.

INTRODUCTION

Morphological variation among individuals has the potential to influence multiple life history characteristics such as dispersal, migration, reproductive fitness, and survival (Wilder et al. 2016). One morphological trait that might be particularly likely to influence these life history characteristics is energetic condition. Individuals that are in better “condition” can disperse or migrate further or more successfully, have greater reproductive success, and survive longer (Heidinger et al. 2010; Liao et al. 2011; Wilder et al. 2016), particularly in years where environmental conditions are harsh (Milenkaya et al. 2015). For example, a study conducted on vipers showed that while the level of fat reserves in males was not related to their sexual activity, females with low fat reserves engaged in sexual interactions less frequently than those with higher fat reserves (Aubret et al. 2002). In contrast, mantids showed conflicting results regarding the relationship between fat reserves and reproductive success (Barry and Wilder 2013). Female mantids were fed either a high protein, low lipid diet, or a high lipid, low protein diet. The females that received the high lipid diet had higher lipid content in most parts of their body compared to that of their high protein diet counterparts. However, they were not able to produce even half as many eggs as the females fed the high protein, low lipid diet. This led to lower male attraction, measured by the number of copulation events, thus negatively impacting further reproductive success.

A variety of morphological proxies have been used to quantify energetic condition (i.e., fat score, weight, ratio of weight to tarsus length, ratio of weight to wing chord length; Labocha et al. 2014). However, there is mixed support regarding whether and how these proxies relate to life history characteristics (Labocha et al. 2014; Wilder et al. 2016). A review conducted by Barnett (2015) shows that, while mass or body size measures of condition are often assumed to have a positive linear relationship with fitness, this is not always the case, and the relationship should first be empirically validated before being used as a proxy (Barnett et al. 2015). In some instances, the condition proxy might relate to life history characteristics, but in an unexpected way. For example, theoretical simulations of small birds show that survival does not increase linearly with energy (i.e., fat) reserves (McNamara et al. 2005). If the reserves are too low, the individual is at risk of starvation. However, once the reserves get too high, the individual is at an increased risk of predation (McNamara et al. 2005). Thus, fat reserves can relate to a life history variable (survival), but in a U-shaped relationship rather than a linear one.

Although some studies use multiple morphological proxies for condition (e.g., Warnock and Bishop 1998), rarely are these variables directly compared. Multiple proxies should correlate with each other if they measure the same trait (energetic condition). Furthermore, there is still confusion about what trait some proxies actually measure. For example, a study conducted on two species of crickets showed that three estimates of body condition based on fat content or on the relationship between body mass and body length (scaled mass index or ordinary least squares regression) did not correlate with each other (Kelly et al. 2014), thus indicating that they do not measure the same trait. This is an example of the jingle fallacy (Block 1995; Carter et al. 2013), where a single trait label (“condition”) actually encompasses more than one distinct trait. In this case, two investigations using different proxies can be conducted on the same research question, using the same species, but may end up with different results. This is problematic because inconsistency in results among researchers can result in potentially misleading interpretations of the impact of variation in morphology in relation to life history and population variables (Stevenson and Woods Jr 2006).

Here we compare two indices (fat score and the scaled mass index) of an individual’s energetic state to validate whether they correlate with each other, which would indicate that they both measure body condition. Fat score, as described by Kaiser (1993), is a numerical estimate of the amount of fat visible under the skin (Fig. 1). The score ranges from 0 to 8 depending on the size and appearance of the fat located in the individual’s abdomen and interclavicular depression, with 0 indicating no visible fat and 8 indicating extensive fat covering the ventral surface such that no muscle tissue is visible. For example, a score of 1 corresponds to sparse traces of fat visible in the interclavicular depression and abdomen. This measure is frequently used in birds (Merilä and Svensson 1997; Erciyas et al. 2010; Cornelius Ruhs et al. 2019), and is a straightforward, non-invasive method for estimating condition. However, previous research found that it does not always positively relate with life history variables. For example, Haas (1998) found no difference between fat scores in individuals that had successful or failed nests in American robins and brown thrashers, indicating that fat score may not explain much of the variation in nest success in some species. Further research is needed to understand the relationship between fat score measures and life history characteristics.

In contrast, the scaled mass index (SMI) is more difficult to calculate than the fat score, but it has become the predominant ratio method for quantifying energetic condition within and among populations (Maceda-Veiga et al. 2014; Delciellos et al. 2018; English et al. 2018). The SMI is an individual’s mass scaled by skeletal body size (Peig and Green 2009). Unlike the common alternative which uses a simple ratio of tarsus (lower leg) length to body mass, the SMI accounts for the tendency towards allometric scaling where the relationship between body mass and structural size increases by a power law (Huxley 1932). When individuals with different structural body sizes can be standardized to the population average structural body size, then energetic condition (the amount of mass not explained by structural body size) can be more directly compared within and across populations. That is, the SMI calculates the energetic condition as the mass of an individual relative to the population by first computing the mass that the individual would have at the population average of a specific body measurement (e.g., tarsus length). Next, structural body size of the individual is standardized by scaling the individual’s structural body length by the population average of that body measurement, which accounts for population differences. The SMI is calculated as: \(Mass_i\left[ \frac{AvgLength_p}{Length_i} \right]^{slope_p}\) where \(Mass_i\) is each individual’s weight in grams, \(Length_i\) is the value of the chosen measure of structural body length for each bird, \(AvgLength_p\) is the average structural body length in the population, and \(slope_p\) is calculated from the standard major axis regression (which is used to compare variables that were both directly measured and thus have residual error) of a structural body size measure, like tarsus length, on mass (Peig and Green 2009), and is interpreted as the expected change in structural length for a one unit increase in mass. Therefore, individuals in better energetic condition (larger weight for their structural body size) will have a higher SMI compared to individuals in poor condition. Studies across taxa found that the SMI relates positively to reproductive success and survival. For example, mallards with a lower SMI had lower rates of survival compared to their higher SMI counterparts (Champagnon et al. 2012), while in crimson finches SMI was positively related to the number of young that survived to independence (Milenkaya et al. 2015).

Our research will determine whether these two indices of energetic condition measure the same trait, and whether this trait relates to an important life history characteristic: reproductive success. Measuring reproductive success in birds involves finding and monitoring nests (Mayfield 1961). However, nests are usually built in cryptic locations and parents behave secretly (Gill 1995), thus making it difficult to quantify the number of eggs and nestlings inside the nest over time. Additionally, it is difficult and time-consuming to track the survival of offspring once they leave the nest. Therefore, we will use the predominant method in this field for quantifying reproductive success: whether a nest fledged offspring (Mayfield 1961).

Our study system is a population of great-tailed grackles (Quiscalus mexicanus), hereafter “grackles”, in Tempe, Arizona. This system is ideal for this investigation because grackles are native to the tropical climates of Central America (Johnson and Peer 2001), but have rapidly expanded their geographic range into new areas (Wehtje 2003). Because grackles are a water-associated species, the desert habitat of Tempe presents physiological challenges that could lead to an increased likelihood of a tradeoff between survival and reproductive attempts (Henderson et al. 2017). Deserts are characterized by a scarcity of water and extreme temperature fluctuations, which require behavioral and physiological adaptations (Costa 2012). Wide variation in body condition and reproductive success is possible if grackle physiology requires more water than is present in the environment, and some individuals may cope with physiological stress, or find hidden sources of water, better than others (Henderson et al. 2017).

Figure 1: A male grackle showing the yellow/orange tint of fat under the skin in the intraclavicular depression (left); and a female grackle showing no fat under the skin of the intraclavicular region, but significant fat deposits under the skin of the abdomen (right).

HYPOTHESES

We measured two proxies of body condition and observed reproductive success in grackles to test two hypotheses:

H1 - There is a relationship between two different morphological indices of condition: fat score and the scaled mass index.

Prediction 1: Fat score and the scaled mass index will be positively correlated. This would indicate that these two indices measure the same trait, and it is likely they both are proxies for fat content.

Prediction 1 alternative 1: There is a negative correlation between fat score and the scaled mass index. This would indicate that there may be a tradeoff between the two indices where a larger value of the scaled mass index may measure muscle content rather than fat, and individuals with more muscle have less visible fat.

Prediction 1 alternative 2: There is no correlation between fat score and the scaled mass index. This indicates that these two variables do not measure the same trait. Fat score may not adequately capture a bird’s condition because birds may be selected to only store the minimal fat necessary to prevent starvation, while also minimizing the weight gain that would make them easier targets for predators (Barnett et al. 2015). Similarly, the scaled mass index could be heavily influenced by body size, therefore reflecting structural size rather than fat storage (Labocha and Hayes 2012).

H2 - Condition (as measured by fat score and the scaled mass index) relates to reproductive success (measured as a binary variable of whether a female had one or more fledglings (1) or not (0), and whether a male defended a territory containing nests (1) or not (0)).

Prediction 2: Morphological indices of condition (fat score and the scaled mass index) will correlate positively with reproductive success. This would indicate that individuals with more fat, and therefore higher energy reserves, are better able to acquire the resources necessary for reproduction.

Prediction 2 alternative 1: Morphological indices of condition (fat score and the scaled mass index) will correlate negatively with reproductive success. This indicates that individuals may make trade offs, with some acquiring more food and increasing their energy reserves, and others prioritizing reproductive activities over increasing energy reserves.

Prediction 2 alternative 2: Morphological indices of condition (fat score and the scaled mass index) do not correlate with reproductive success. This indicates that other, potentially non-morphological, individual characteristics relate to reproductive success (i.e., cognition, nest site selection, breeding experience, predator vigilance, etc.).

ASSOCIATED PREREGISTRATION

This preregistration used secondary data that were collected as part of other ongoing investigations (tarsus length in http://corinalogan.com/Preregistrations/g_flexgenes.html; tarsus length, body weight, number of fledglings, and whether a male holds a territory in http://corinalogan.com/Preregistrations/g_withinpop.html; and tarsus length in http://corinalogan.com/Preregistrations/g_expansion.html). This preregistration, containing the hypotheses, methods, and analysis plan, was written (July 2019) and submitted to Peer Community In Ecology for pre-study peer review (August 2019) before any analyses were conducted. We revised according to reviewer comments and received in principle acceptance by PCI Ecology of the version on 8 Nov 2019. After that, we conducted the analyses in the preregistration. Our final methods, results, and discussion, including all data and code, are listed below.

After pre-study peer review: Deviations from the planned methods

  1. We realized that the sexual dimorphism of male and female body sizes necessitates separate analyses. Therefore, we calculated SMI for males and females separately, and ran separate models for each sex for the repeatibility analysis (P1 and P2).

  2. Fat score data were distributed such that the majority of scores were 0, with some 1’s and very few higher numbers. This lack of variance in the response variable led to problems when we ran the models: it was difficult to fit models using an ordinal regression. The function “simulateResiduals”, which we used to check our data, does not work with data in the ordinal family. Consequently, we modified the model to use a logistic regression where the dependent variable FatScore is categorized as individuals that showed no visible fat (y = 0), or some fat was present (y = 1) where we combined all individuals that had fat score values of 1 or greater. Subsequent data checking indicated that these data were not zero-inflated or overdispersed.

P1: correlation between SMI and Fat score

  1. Warning messages occurred during the repeatability analysis using the “rptR” package in R (Stoffel et al. 2017) indicating that the fit was singular, likely because the variance for the Experimenter random effect in the model for both female and male wing length was 0.001. We thus conducted an unregistered analysis where we confirmed that our repeatability values from the repeatability models were valid, despite the warning, by hand calculating repeatability following Nakagawa and Schielzeth (2010). The hand-calculated repeatabilities were nearly identical (female R = 0.5, male R = 0.71) to the output from the rpt function.

  2. Despite the data checking which indicated our model was not overdispersed or zero inflated, we could not get the fixed effects or random effect to converge using the Bayesian package in R “MCMCglmm”. We found no improvement in model fit by tweaking the priors or iterations/burnin/thin options. Therefore, we fit these models using the function glmer, a frequentist framework.

  3. The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

P2: body condition and reproductive success

  1. Only two females had reproductive success data from more than one year in our study (2019 and 2020). Consequently, there were very few repeated measures in this sample and our random effect of bird ID accounted for zero variance. This led to a warning that our model fit was singular. Therefore, we removed the data for these females for 2020 so we could remove ID as a random effect from the model, which resulted in the model running without warnings. We removed the 2020 data for these females because their condition data was collected in 2019 and these measures were more likely to relate to their 2019 reproductive success data than to their reproductive success in 2020.

  2. The fit of the model analyzing the relationship between body condition and male reproductive success (ability to hold a territory containing female nests) was singular. The Year random effect accounted for zero variance in the data, so we removed it. The fit was still singular, but we retained the ID random effect (although it also explained zero variance) to account for repeated measures in this sample.

  3. The model fit was again singular in our logistic exposure model because the Year random effect explained zero variance in the data. We removed this random effect from the analysis.

RESULTS

Prediction 1: correlation between SMI and Fat Score

We were able to calculate SMI for 24 males and 62 females, and fat score values were available for 21 males and 47 females.

We found that wing length was more tightly correlated with body mass than tarsus length in both sexes, therefore we used wing length in our SMI calculations (female n = 62, r = 0.26, p = 0.03; male n = 24, r = 0.35, p = 0.08). This allows us to account for as much variation in body mass as possible that is associated with skeletal body size because leftover variation in body mass is more likely to relate to energetic condition. Consequently, we used wing length in our calculation of SMI as: \(Mass_i\left[ \frac{AvgWing_p}{AvgWing_i} \right]^{slope_p}\). \(Mass_i\) is each individual’s weight in grams, \(AvgWing_i\) is the average value of the measures of the left and right wing lengths of each bird, \(AvgWing_p\) is the average wing length in the population, and \(slope_p\) is the value of the slope from a standard major axis regression of the population’s wing length on the population’s mass (Peig and Green 2009).

To validate that we were measuring structural body size consistently across experimenters, we analyzed the repeatability of wing length in the birds in our sample that were measured more than once. We found that average wing length was repeatable (n = 17 females, Repeatability ± standard error = 0.53 ± 0.18; n = 18 males, Repeatability ± SE = 0.75 ± 0.11). Data permutations and a likelihood ratio test both confirmed that these repeatability values were statistically significant at p < 0.01.

We found that fat score was not correlated with SMI, which indicates that they are not measuring the same trait (female p = 0.81; male p = 0.50; Table 1). There was also no relationship between season (breeding or non-breeding) and female fat score (p = 0.71). Only 2 males were measured during the breeding season, therefore we omitted season as an independent variable in the male model.

Table 1. Results from the logistic mixed-effect regression for females and fixed-effect regression for males to determine whether fat score and scaled mass index (SMI) are correlated. Estimates are presented with the standard error in parentheses. Our sample size was too small to test for a season effect in males.
Females
Males
Parameter Estimate (SE) p-value Estimate (SE) p-value
Intercept -0.20 (0.74) 0.79 -0.82 (0.64) 0.21
SMI 0.07 (0.30) 0.81 0.46 (0.62) 0.46
Season 0.27 (0.71) 0.70 NA NA

P2: body condition and reproductive success

Our sample size for P2, where individuals had measures of reproductive success, SMI, and fat scores, was 20 for females and 20 for males.

In some investigations, body condition shows a non-linear relationship with reproductive success (Milenkaya et al. 2015). To test for this, we calculated the SMI categories using 0.5 standard deviation (sd) increments around the mean to determine whether individuals in some categories were more likely to be reproductively successful (Fig. 2). Category 1 includes “low” SMI values that are more than 1 sd less than the mean, category 2 is “moderately low” SMI values and ranges from 0.5 sd to 1 sd less than the mean, category 3 includes “moderate” SMI values between 0.5 less than the mean and 0.5 sd greater than the mean, category 4 includes “moderately high” individual SMI values between 0.5 and 1 sd greater than the mean, and category 5 includes “high” SMI values that are more than 1 sd greater than the mean. However, we found no evidence for a non-linear relationship between reproductive success and SMI for males or females (Fig. 3).

Figure 2: Frequency histogram of the SMI scores for males and females. The mean SMI value is indicated by a red vertical line. We created SMI category bins (indicated with vertical blue lines) in 1 standard deviation increments, centered on the mean. Category 3 indicates the SMI value is close to the population mean value. Categories 1 and 2 are individuals that have SMI scores that are low, and moderately low, respectively, compared to the population mean value. Similarly, categories 4 and 5 contain individuals that have SMI scores that are moderately high and high, respectively, compared to the population mean value.

Figure 3: The proportion of individuals that successfully fledged nests (females: left) or held a territory (males: right) in low (1), moderately low (2), moderate (3), moderately high (4) and high (5) scaled mass index (SMI) categories. Dots are sized according to the number (n) of individuals in that category. There is no evidence of a non-linear relationship.

We used linear models to determine whether season would be important to include in our models testing P2. We found that neither SMI (female p = 0.26, male p = 0.15) nor fat score (female p = 0.68, male p = 0.99) differed by season in females or males (Fig. 4). Although we note that, as stated above, we lack sufficient fat score data from males in the breeding season so results from that model should be interpreted with caution. Consequently, we did not include season as an independent variable in the P2 models.

Figure 4: Scaled mass index (SMI) was not significantly different between the breeding and non-breeding seasons for either sex.

Because fat score and SMI did not correlate, we included both as independent variables in our models testing prediction 2. We found that neither SMI (p = 0.13), nor fat score (p = 0.82) was associated with whether a female fledged offspring (Table 2). There was also no evidence of a relationship between the ability of a female to produce fledglings and having previously spent time in the aviaries (p = 0.22). For males, the ability to defend a territory was also unrelated to either SMI (p = 0.13) or fat score (p = 0.76). Additionally, we found that those males who spent more time in the aviaries were less likely to hold a territory compared with males who were never in the aviaries or who spent less time in the aviaries (p = 0.02). However, we stress that our sample size was relatively small (20 males), and we did not have a balanced sample because there were no males that did not defend a territory and were never in the aviaries. Additionally, only five males had data from more than one breeding season, which resulted in our model fit being singular because the random effect for bird ID accounted for essentially zero variance. However, we kept ID in the model to account for the repeated samples.

Table 2. Results from the logistic regression for females and males to test whether reproductive success relates to condition. Estimates are presented with the standard error in parentheses.
Females
Males
Parameter Estimate (SE) p-value Estimate (SE) p-value
Intercept -0.02 (0.73) 0.98 3.05 (1.40) 0.03
FatScore 0.15 (1.02) 0.89 -0.33 (1.10) 0.77
SMI -0.92 (0.61) 0.13 1.18 (0.78) 0.13
Aviary -1.38 (1.14) 0.23 -3.62 (1.56) 0.02*

P2: body condition and probability of daily nest survival

Logistic regression analyses to determine reproductive success from nests discovered in different stages will be systematically biased (Shaffer 2004). Nests discovered at a more progressed stage (i.e., nestling stage compared to building stage) are statistically more likely to succeed and nests with frequent and prolonged adult visits (such as those that occur when nests survive longer) are more likely to be discovered. Therefore, nests that fail early are less likely to be detected (Shaffer 2004). Consequently, we analyzed female reproductive success using a logistic exposure model (Bolker 2014), which uses survival analysis to determine the factors affecting the probability of daily nest survival, while accounting for incomplete nest observations. We found that the probability of daily nest survival was significantly negatively related to SMI (p = 0.03; Table 3), where, for every unit increase in SMI, the odds of daily nest survival decreased by half. This indicates that a female with a larger SMI (more mass for her structural body size) was less likely to have her nest survive each day (Fig. 5). There was no statistically significant relationship between the probability of daily nest survival and fat score, day of the year, or time spent in the aviaries (Table 3). Although not statistically significant, the effect size for the relationship between fat score and daily nest survival is large (Fig. 5) and potentially biologically meaningful. The odds of a nest surviving on a given day are almost 2.5 times greater for birds with some fat (a score of 1) compared to no fat (a score of 0).

Table 3. Results of the logistic exposure model showing the relationship between the probability of daily nest survival and scaled mass index (SMI), fat score, the amount of time spent in the aviaries, and the day of the year. Parameter estimates are presented with the standard error in parentheses. Odds ratios (OR) represent the exponentiated estimates, and are presented to increase interpretability with 95% confidence intervals in parentheses.
Parameter Estimate (SE) OR (CI) p-value
Intercept 1.99 (0.40) 7.32 (3.3-16.0) <0.001
Fat score 0.91 (0.49) 0.50 (0.27-0.92) 0.06
SMI -0.69 (0.31) 2.48 (0.95-6.49) 0.03*
Day of year -0.21 (0.15) 0.63 (0.19-2.10) 0.16
Aviary -0.47 (0.61) 0.81 (0.60-1.10) 0.44

Figure 5: Odds ratios for independent variables affecting the probability of a nest surviving a given day. The dots and corresponding values represent the odds ratio values, and lines represent the confidence intervals around the odds ratio value. The vertical line at x = 1 delineates the odds ratio value for no relationship between the estimates and the probability of daily nest survival. The asterisk indicates an odds ratio value that is statistically significant.

DISCUSSION

Although it is often implicitly assumed that most condition proxies measure the same trait, we found that two proxies of energetic condition, fat score and SMI, did not correlate with each other in the great-tailed grackle, regardless of whether it was the breeding or non-breeding season. Further, we found that neither fat score nor SMI correlated with a female’s ability to produce fledglings or a male’s ability to hold a territory containing nests. However, we did find that the probability a female’s nest will survive a given day is negatively related to SMI. These results have implications for the interpretation of results that are based on such proxies and for the use of these proxies in future research.

There are several potential reasons why grackle fat score and SMI did not correlate. First, it is possible that we were unable to accurately measure the amount of fat the birds actually stored. In addition to storing fat under their skin, birds may also store fat intraperitoneally (Musacchia 1953), which would not have been detected with our fat score measure. Second, variation in mass among grackles might have resulted from not only variation in fat content, but also from variation in muscle content (Labocha and Hayes 2012). However, measuring muscle content requires destructive methods (i.e. sacrificing the birds; Zhang et al. 2015), which was beyond the scope of the current research program. Third, it is possible that fat score and SMI did not correlate due to experimenter error in collecting these measurements. We were unable to quantify the repeatability of our measures within and between experimenters because we did not collect repeated measurements on the same grackles when they were in hand (to reduce the amount of processing time a bird experiences). Finally, our sample size might have been too small to detect an effect. However, the effect size for the relationship between fat score and SMI was essentially zero (0.001), therefore it is unlikely that a larger sample size would find a biologically informative relationship between these two proxies.

Although our first analysis of reproductive success, measured as the ability to produce fledglings (females) or to hold a territory containing nests (males), found no correlation with fat score or SMI, when we used logistic exposure models to determine whether female body condition related to the probability of daily nest survival, we found a negative relationship between SMI and the likelihood of daily nest survival. This result was surprising, but could be due to larger females actually carrying proportionally smaller energetic reserves than their smaller female counterparts, as seen in red-winged blackbirds (Langston et al. 1990). In some species, females with smaller body sizes are able to initiate breeding earlier because they can allocate more resources to reproduction compared to larger individuals that have higher bodily energy demands and therefore fewer excess energetic resources (Murphy 1986; Langston et al. 1990; Barbraud et al. 2000). This indirectly affects reproductive success because nesting earlier increases the probability of nesting success and multiple nesting attempts (Perrins 1970; Johnson and Peer 2001). Yet, we found no relationship between the probability of daily nest survival and day of the year, therefore this is unlikely to explain the negative relationship between SMI and nest survival. Alternatively, it is possible that larger females are unable to build a more concealed nest in the most dense vegetation, or that larger females are more likely to disrupt nest stability. The grackle nests were very high (often >10m above ground) and usually fairly well concealed, so we could not determine the causes of nest failure. Further investigations would be required to determine how body condition relates to specific threats to nesting success. In addition, the parameter estimate for the relationship between fat score and the daily probability of nest survival indicates that females with some visible fat are more than twice as likely to have a nest survive a given day. Because the direction of this effect is opposite to the relationship between SMI and nest survival, this is further evidence that these two proxies represent different traits, and that SMI is likely influenced by muscle mass.

Measurements of energetic condition are important for understanding variation in life history characteristics in studies across the animal kingdom. However, the results of this study highlight the need to better understand proxy measures of condition, not only in grackles, but for birds in general. Most studies on avian energetic condition only use one proxy for condition, but because energetic condition is difficult to measure directly, it is important to compare multiple proxy variables to ensure each proxy is measuring the intended trait (the jingle-jangle fallacy; Block 1995; Carter et al. 2013). Future research could add to this work by incorporating additional methods to measure energetic condition, for example, blood hematocrit levels (Dawson and Bortolotti 1997), protein storage (Houston et al. 1995), or by studying additional traits that could relate to variation in energy stores, such as dispersal (Ellers et al. 1998) or survival (Liao et al. 2011). Furthermore, future research would benefit from using logistic exposure models to examine the relationship between body condition and reproductive success because these models control for the bias that arises when early nest failures are not detected, which is not possible in logistic regression models, and it is more sensitive to changes in a bird’s nest status (Shaffer 2004).

METHODS

The methods below are based on the preregistration, with small changes as described in the Deviations from the preregistration section above.

Planned Sample

Great-tailed grackles are caught in the wild in Tempe, Arizona using a variety of methods (e.g., walk-in trap, bownet, mist net). After capture we immediately process birds by attaching colored leg bands in unique combinations for individual identification, conducting morphological measurements of weight, tarsus length, flattened wing length, tail length, skull length, bill length and fat score (the amount of visible fat under the skin in the clavicle and abdomen as in Kaiser 1993). Most grackles are released after completion of color band marking, measurements, and acquiring a blood sample. A subset of grackles are held in aviaries for up to 6 months for behavioral testing, and then released back to the wild at their location of capture.

From March - August, we monitor the behavior of all color-marked grackles to determine their nesting status. We follow females carrying nesting materials to find their nest. We determine whether the male territory owner is color-marked as well. Then we check each nest approximately every day to determine the status based on the female’s behavior (building, incubation, feeding nestlings, feeding fledglings, failed).

Individuals included in this sample will be those for which we have measures of condition when they were adults. We will not include individuals whose data were collected as juveniles. As of 30 July 2019, we have fledgling data for 14 females that exhibited breeding behavior (5 had 1+ fledgling, 9 had no fledglings) and breeding territory status for 10 males (7 territory holders, 3 non-territory holders, 2 not observed so not part of this sample). Therefore, the minimum sample size for H2 will be 24. The minimum sample size for H1 will be 72, because that is how many marked individuals we have biometric data for so far. However, we expect to be able to add to the sample size for both H1 and H2 before the end of this investigation in Tempe, Arizona. UPDATE Oct 2020: In the second breeding season we had 20 females and 20 males with reproductive success and body condition data.

Sample size rationale

We will continue to color mark as many grackles as possible, and collect biometric data and fat scores. Our current sample of reproductive success is small because the grackles in Tempe nest in very tall palms, making it difficult to determine nest status. However, we plan to collect additional reproductive success data during the breeding season in summer 2020. UPDATE Oct 2020: In the second breeding season we had 20 females and 20 males with reproductive success and body condition data.

Data collection stopping rule

We will stop collecting data for this project in early August 2020 when research at the Tempe, Arizona field site will be finished.

Open data

All data (Berens 2020) are available at https://knb.ecoinformatics.org/view/doi:10.5063/F1NZ862D and at github (the provided code will load these files directly from github).

Randomization and counterbalancing

There is no randomization or counterbalancing in this investigation.

Blinding of conditions during analysis

No blinding is involved in this investigation.

Dependent Variables

P1: correlation between fat and the scaled mass index

  1. Fat score (the amount of visible fat under the skin in the clavicle and abdomen reported as a score from 0 (no fat) to 8 (fat completely covers muscles and underside of the bird); Kaiser 1993) UPDATE Oct 2020: Fat score was heavily 0 skewed with few scores greater than one. To increase model fit we used a binomial response variable instead, where 0 is no fat and 1 is some fat observed undert the skin.

P2: condition and reproductive success

  1. Female had one or more fledglings (yes, no)

  2. Male held a territory consisting of 1 to 3 clumped palms containing nests (yes, no)

Independent Variables

P1: correlation between fat and the scaled mass index

  1. Scaled mass index using measures of body weight and tarsus length or flattened wing length (average of left and right as in Bleeker et al. 2005). We will choose the measure that is most correlated with body weight (Peig and Green 2009).

  2. Season (non-breeding [Sep-Feb], breeding [Mar-Aug]). UPDATE Oct 2020: The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

  3. Random effect: Experimenter (because several different experimenters measure dependent variables on multiple different birds)

P2: condition and reproductive success

  1. Fat score
    • Note 1: if the fat score and the scaled mass index are positively correlated, then we will use only fat score in the model for P2. If they are not positively correlated, then we will add the scaled mass index as an independent variable in the P2 analysis
    • Note 2: if fat score and/or the scaled mass index vary by season (breeding or non-breeding), then we will only use the data from the breeding season to ensure that less time has elapsed between the collection of condition and reproductive success variables

  2. Temporarily held in aviaries for behavioral testing at any point during this study, because this may affect breeding behaviors (yes, no)

  3. Random effect: Year (to determine whether conditions in a given breeding season similarly affected all grackle behavior and nest success)

  4. Random effect: Bird ID (because there may be multiple measures of reproductive success for each bird)

ANALYSIS PLAN

UPDATE Oct 2020:

1) We realized that the sexual dimorphism of male and female body sizes necessitates separate analyses. Therefore, we calculated SMI for males and females separately, ran separate models for each sex for the repeatibility analysis, P1 and P2.

2) Fat score data were distributed such that the majority of scores were 0, with some 1’s and very few higher numbers. This made it difficult to fit models using an ordinal regression. The function simulateResiduals, which we used to check our data, does not work with data in the ordinal family. Consequently, we used logistic regression where the dependent variable FatScore represents no fat (score = 0), or some fat (score = 1)

3) Despite the data checking which indicated our model was not overdispersed or zero inflated, we could not get the fixed effects or random effect to converge using the Bayesian MCMCglmm. We found no improvement in model fit by tweaking the priors or iterations/burnin/thin options. Therefore, we fit these models using the function glmer, a frequentist framework.

4) The Season variable only includes 2 males in the breeding season category, thus we do not have a large enough sample to produce reliable estimates. We removed the Season variable from the model for males.

We will exclude data that was collected from the grackles when they were released from the aviaries to avoid any confounds due to their time in the aviary (e.g., perhaps unlimited nutritious food in the aviaries decreased their fat score). However, to validate that our measures of structural body size (tarsus length or wing length) are precise and accurate, we will measure twice a subset of grackles brought into aviaries - once when they are initially caught, and again up to 6 months later when we release them. We will then calculate the repeatability of these multiple measures. All other data included in this study will come only from wild-caught grackles (including the birds that were brought into the aviaries on their first capture). When missing data occur, the existing data for that individual will be included in the analyses for which their data exist. Analyses will be conducted in R (current version 3.6.3; R Core Team 2017).

Ability to detect actual effects

To begin to understand what kinds of effect sizes we will be able to detect given our sample size limitations, we used G*Power (v.3.1, Faul et al. 2007, @faul2009statistical) to conduct power analyses based on confidence intervals. G*Power uses pre-set drop down menus and we chose the options that were as close to our analysis methods as possible (listed in each analysis below). Note that there were no explicit options for GLMMs, thus the power analyses are only an approximation of the kinds of effect sizes we can detect. We realize that these power analyses are not fully aligned with our study design and that these kinds of analyses are not appropriate for Bayesian statistics (e.g., our MCMCglmm below), however we are unaware of better options at this time. Additionally, it is difficult to run power analyses because it is unclear what kinds of effect sizes we should expect due to the lack of data on this species for these particular research questions.

Data checking

The data will be checked for overdispersion, underdispersion, zero-inflation, and heteroscedasticity with the DHARMa R package (Hartig 2019) following methods by Hartig.

P1 analysis: correlation between fat and the scaled mass index

We will calculate the scaled mass index as described by Peig and Green (2009) using either tarsus or flattened wing length - whichever measure is most correlated with body weight (Peig and Green 2009).

We use a Generalized Linear Mixed Model (GLMM; MCMCglmm function, MCMCglmm package; (Hadfield 2010)) with an ordinal distribution (for categorical variables in MCMCglmm) and probit link using 130,000 iterations with a thinning interval of 10, a burnin of 30,000, and minimal priors (V=1, nu=0) (Hadfield 2014). We will ensure the GLMM shows acceptable convergence (lag time autocorrelation values <0.01; Hadfield 2010), and adjust parameters if necessary to meet this criterion. We will determine whether an independent variable had an effect or not using the Estimate in the full model.

Where we have multiple measures of tarsus or flattened wing length, we will check that our measurements are repeatable using the rptR package (Stoffel et al. 2017).

To roughly estimate our ability to detect actual effects (because these power analyses are designed for frequentist statistics, not Bayesian statistics), we ran a power analysis in G*Power with the following settings: test family=F tests, statistical test=linear multiple regression: Fixed model (R^2 deviation from zero), type of power analysis=a priori, alpha error probability=0.05. We changed the power and the effect size until we reached an output that we project our sample size will be (n=90). The number of predictor variables was restricted to only the fixed effects because this test was not designed for mixed models. The protocol of the power analysis is here:

Input:

Effect size f² = 0.15

α err prob = 0.05

Power (1-β err prob) = 0.86

Number of predictors = 3

Output:

Noncentrality parameter λ = 13.3500000

Critical F = 2.7119214

Numerator df = 3

Denominator df = 85

Total sample size = 89

Actual power = 0.8635760

This means that, with a sample size of 89, we would have an 86% chance of detecting a medium effect (approximated at f2=0.15 by Cohen 1988).

code shown in .rmd

P2 analysis: condition and reproductive success

To model the effect of body condition on reproductive success, we will use two types of logistic mixed-effect models. Both types are supported in the literature, but are slightly different in the way in which the link function is specified. First, we will model reproductive success using a generalized linear mixed model framework with a logit link function (i.e. Milenkaya et al. 2015). We will also use a logistic exposure model that has a link function which accounts for the time interval between nest checks when estimating the probability of daily nest survival (Shaffer 2004; Bolker 2014). If fat score and the scaled mass index are positively correlated in P1, then we will use only fat score as the independent variable in this GLMM. If they are not positively correlated, we will include both as independent variables.

Previous research found a non-linear relationship between reproductive success and body condition variables (Milenkaya et al. 2015). To check whether this is occurring in our data, we will first plot our raw data to determine if we need to include a non-linear body condition independent variable into our model (i.e. FatScore2). Our dependent variable is binary, so to more clearly see the trends in the data, on the x-axis we will bin our condition scores into 5 categories based on standard deviations (sd) around the mean (low = < 2 sd, moderately low = -2 sd to -1 sd, moderate = -1 sd to +1 sd, moderately high = +1 sd to +2 sd, high = > 2 sd). Then on the y-axis we will use the proportion of individuals in each category that had successful nests. UPDATE Oct 2020: Because most individuals fell within the medium category when we grouped data using 1 standard deviation around the mean, we switched to using half standard deviation increments around the mean.

A power analysis was conducted as above for P1 and the protocol reported here:

Input:

Effect size f² = 0.15

α err prob = 0.05

Power (1-β err prob) = 0.90

Number of predictors = 2

Output:

Noncentrality parameter λ = 13.2000000

Critical F = 3.1038387

Numerator df = 2

Denominator df = 85

Total sample size = 88

Actual power = 0.9020264

This means that, with a sample size of 88, we would have a 90% chance of detecting a medium effect (approximated at f2=0.15 by Cohen 1988).

code shown in .rmd

Do body condition variables vary by season?

code shown in .rmd

Does body condition relate to reproductive success?

code shown in .rmd

Does female body condition relate to the probability of daily nest survival?

Our measure of female nest success could be systematically biased against nests that failed early (Shaffer 2004). Consequently, we also analyzed female reproductive success using a logistic exposure model. This type of model determines the factors affecting daily nest survival probability.

code shown in .rmd

ETHICS

This research is carried out in accordance with permits from the:

  1. US Fish and Wildlife Service (scientific collecting permit number MB76700A-0,1,2)
  2. US Geological Survey Bird Banding Laboratory (federal bird banding permit number 23872)
  3. Arizona Game and Fish Department (scientific collecting license number SP594338 [2017], SP606267 [2018], and SP639866 [2019])
  4. Institutional Animal Care and Use Committee at Arizona State University (protocol number 17-1594R)

AUTHOR CONTRIBUTIONS

Berens: Hypothesis development, data collection, revising/editing.

Logan: Study design, write up, revising/editing, materials/funding.

Folsom: Data collection, revising/editing.

Sevchik Data collection, revising/editing.

Bergeron: Data collection, revising/editing.

McCune: Hypothesis development, data collection, data analysis, write up, revising/editing.

FUNDING

This research is funded by the Department of Human Behavior, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology.

ACKNOWLEDGEMENTS

We thank our research assistants for help with trapping the grackles and collecting the biometric and nest/territory data: Aelin Mayer, Nancy Rodriguez, Brianna Thomas, Aldora Messinger, Elysia Mamola, Michael Guillen, Rita Barakat, Adriana Boderash, Olateju Ojekunle, August Sevchik, Justin Huynh, Amanda Overholt, and Michael Pickett.

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